The inevitable leakage of privacy as a result of unrestrained disclosure of personal information has motivated extensive research on robust privacy-preserving mechanisms. However, existing research is mostly limited to solving the problem in a static setting with disregard for the privacy leakage over time. Unfortunately, this treatment of privacy is insufficient in practical settings where users continuously disclose their personal information over time resulting in an accumulated leakage of the users' sensitive information. In this paper, we consider privacy leakage over a finite time horizon and investigate optimal strategies to maximize the utility of the disclosed data while limiting the finite-horizon privacy leakage. We consider a simple privacy mechanism that involves compressing the user's data before each disclosure to meet the desired constraint on future privacy. We further motivate several algorithms to optimize the dynamic privacy-utility tradeoff and evaluate their performance via extensive synthetic performance tests.
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